A function is a named body of commands that do significant work, converting input arguments into a returned answer. When we define a function, we should summarize its knowledge-production capabilities. This is critical when we assemble a program from a collection of functions, because we connect functions together based on what they are capable of doing.
A function is a ``mini-program'': it has inputs, namely the arguments assigned to its parameters, and it has outputs, namely the information returned as the function's answer. It acts like a logic gate or chip, with ``input wires'' and ``output wires.''
Here is an example that shows the format we use for writing a function:
=================================================== def recip(n) : # HEADER LINE : LISTS NAME AND PARAMETERS """recip computes the reciprocal for n""" { pre n != 0 # PRECONDITION : REQUIRED PROPERTY OF ARGUMENTS post ans == 1.0 / n # POSTCONDITION : THE GENERATED KNOWLEDGE return ans # VARIABLE USED TO RETURN THE RESULT } ans = 1.0 / n # BODY (no assignment to parameter n allowed) return ans ===================================================The precondition states the situation under which the function operates correctly, and the postcondition states what the function has accomplished when it terminates.
For simplicity, we require that no assignment is made to a parameter inside the body of the function. In a call-by-value language like Python and Java, this is never a handicap.
The functions's specification consists of its header line and its pre-, post-condition, and return lines; it tells us how to use the function correctly: (The comment with the informal description is also nice to have.) Here is the specification for the above function:
def recip(n) : """recip computes the reciprocal for n""" { pre n != 0 post ans == 1.0 / n return ans }The specification tells us what we need to know to use the function correctly. The person/program who calls the function is not supposed to read the function's code to know how to use the function. This is especially crucial when you use a function from a library that was written by someone else --- you should not read the library's code to learn how to call the library's functions!
Back to the example: Since we do not allow assignments to parameter n within the body of recip, we know that the value of n in the postcondition is the same as the value of n in the precondition --- this makes the postcondition meaningful.
To call the function, we supply an argument that binds to (is assigned to) its parameter, and we supply a variable that is the target for the returned answer. Once again, for this specification:
def recip(n) : """recip computes the reciprocal for n""" { pre n != 0 post ans == 1.0 / n return ans }we correctly call the function like this:
=================================================== a = readInt() if a > 0 : { 1. a > 0 premise 2. a != 0 algebra 1 # this proves recip's precondition } b = recip(a) { 1. b == 1.0 / a premise } # in return, we presume its postcondition print "the reciprocal of", a, "is", b else : . . . ===================================================To call the function, we must prove the function's precondition at the point of call for its argument. As our reward for establishing the precondition, we receive in return the postcondition, stated in terms of a and b: b == 1.0 / a.
For simplicity, we require that the target variable that receives the function's answer does not appear in the function's arguments, e.g., the call, b = recip(b+2), is not allowed, because it confuses the b in the argument to recip with the b in the answer.
In contrast, we cannot presume recip's postcondition in this situation:
=================================================== x = readInt() # We do not know if x is nonzero.... y = recip(x) { ??? } ===================================================Since we did not prove x != 0 as a precondition for applying recip, we cannot assert the postcondition after the call. Literally, we have no knowledge of what the function produces in this situation --- perhaps the call will ``crash'' (generate an exception)!
Here is a summary of what we have developed so far. Given a function, f, with a pre-post specification, we must show that the presumption of the precondition at the beginning of the function's body lets us prove the postcondition at the end of the body:
=================================================== def f(x): { pre Q (where assertion Q can mention x) Call this, f_pre. post R (where assertion R can mention ans and x) Call this, f_post. return ans (the name of the answer variable) } { 1. Q premise } BODY # does not assign to x { ... prove R here, that is, ans and x have property R } ===================================================Outside of the function, f, we write f_pre for Q and f_post for R. Here is the schematic for a correct call of the function. It is a variation of the assignment law:
=================================================== { [e/x]f_pre } # prove the precondition, where e binds to parameter x y = f(e) # y does not appear in e { 1. [y/ans][e/x]f_post premise # presume the postcondition, # where y gets ans's value 2. [y_old/y][e/x]f_pre premise # knowledge before the call is revised . . . } ===================================================Recall that [e/x]f_pre defines a substitution of e for all occurrences of x within formula f_pre.
Here is another example, an absolute-value function:
=================================================== def absValue(x) : { pre x != 0 post ans > 0 and (ans == x or ans == 0 - x) return ans } if x < 0 : ans = 0 - x else : ans = x return ans ===================================================The precondition is crucial to the success of the postcondition (that the answer is positive). We construct a proof that converts the knowledge in the precondition into the knowledge in the postcondition:
=================================================== def absValue(x) : { pre x != 0 post ans > 0 and (ans == x or ans == 0 - x) return ans } if x < 0 : ans = 0 - x { 1. x < 0 premise 2. ans == 0 - x premise 3. ans > 0 algebra 1 2 4. ans == x or ans == 0 - x algebra 2 5. return 3 4 # lets us collect two facts at once } else : { 1. x != 0 premise 2. not(x < 0) premise 3. x > 0 algebra 1 2 } ans = x { 1. ans == x premise 2. x > 0 premise 3. ans > 0 subst 1 2 4. ans == x or ans == 0 - x algebra 1 5. return 3 4 } # in each arm, we proved ans == x or ans == 0 - x and also ans > 0 { 1. ans == x or ans == 0 - x premise 2. ans > 0 premise 3. ans > 0 and (ans == x or ans == 0 - x) andi 2 1 } return ans ===================================================Now that the function is certified to satisfy its specification, we can call it:
=================================================== n = readInt() if n != 0 : { 1. n != 0 premise } m = absValue(n) { 1. m > 0 and (m == x or m == 0 - x) premise 2. m > 0 ande 1 } ===================================================
Function specifications are critical to software libraries: Every library component simply must has a specification that describes how to connect the component into an application. Often the specifications are written in English, but underlying the English descriptions are the pre-postconditions illustrated in this section.
If you the person writing the function for others to use, you supply the function's specification. You can calculate the specification using the laws we studied in the previous chapter. But better still, you should start with the specification and write the function so that it matches the specification.
You start by stating, in English or otherwise, the function's goal. You study the goal and consider how to meet it; you write the code. You take note of the requirements your function needs on its entry parameters and global variables to reach the goal. Finally, you apply the programming-logic laws to show that the coding matches the specification.
Here is a simplistic example. Say you must write a function that receives two numbers as inputs and selects the maximum, or larger, of the two as the function's output. The specification of the function, max, might look like this:
=================================================== def max(x, y) """max selects the larger of x and y and returns it as its answer""" { pre ??? post (ans == x ∨ ans == y) ∧ (ans >= x ∧ ans >= y) return ... } ===================================================You must write the code for function max so that it meets the postcondition. Along the way, you might require some restrictions on x and y for your solution to work. These would be listed in the precondition.
The logical operators in the postcondition sometimes give us hints how to code the function. Here, we see that the function's answer will be either x or y, suggesting that the function will use assignment commands of the form, ans = x and ans = y. We also see that there is an ``or'' operator in the specification. This suggests that we will require an if-command to choose which of the two assignments to use. Although it is not an exact science, we can move from the postcondition to this possible coding:
=================================================== def max(x, y) { pre ??? post (ans == x ∨ ans == y) ∧ (ans >= x ∧ ans >= y) return ans } if x > y : ans = x else : ans = y return ans ===================================================Now, we use the laws for assignments and conditionals to compute whether a precondition is needed that places restrictions on x and y so that the function behaves properly. What we see here is that our coding works correctly with numbers (and with strings, too!). But it might not work correctly, say, if x or y are arrays or pointers. For this reason, it seems safe to use this precondition:
=================================================== def max(x, y) """max selects the larger of x and y and returns it as its answer""" { pre x and y are both numbers or both strings post (ans == x ∨ ans == y) ∧ (ans >= x ∧ ans >= y) return ans } if x > y : ans = x else : ans = y return ans ===================================================Now, we have a function --- a component --- that others can insert into their programs.
def f(x): { pre Q post R return ans }If a call to f is meant to achieve a goal, G, we reason backwards from G to its subgoal like this:
=================================================== { subgoal: [e/x]f_pre ∧ ([e/x][y/ans]f_post —> G) } y = f(e) # y does not appear in argument e { goal: G } ===================================================That is, for the call, f(e), to accomplish G, it must be the case that f's postcondition implies G and that we can prove f's precondition holds true (so that we can call f).
An example: for
=================================================== def recip(n) : { pre n != 0 post ans == 1.0 / n return ans } ===================================================and the program
=================================================== a = readInt() { subgoal: ??? } b = recip(a) { goal: b < 1.0/3 } ===================================================we compute that the subgoal is a != 0 ∧ (b = 1.0/a —> b < 1.0/3). This simplifies to a != 0 ∧ (1.0/a < 1.0/3)), which simplifies to a != 0 ∧ a > 3, which simplifies to a > 3. This tells us what assert or if command to add to our program:
=================================================== a = readInt() assert a > 3 { subgoal: a > 3 } b = recip(a) { goal: b < 1.0/3 } ===================================================That is, if we expect the computed reciprocal to be less than one-third, then we must supply an input int that is 4 or more.
Say that a variable, v, is ``global'' to a function f if v exists prior to a call to f. (More precisely stated, v's cell does not live in f's namespace/activation-record.) For example, pi is global to circumference here:
=================================================== pi = 3.14159 def circumference(diameter) : answer = pi * diameter return answer . . . r = readFloat("Type radius of a circle: ") c = circumference(2 * r) # compute circumference of circle area = r * r * pi # compute circle's area ===================================================A global variable ``lives'' before a function is called and lives after the function finishes. In the above example, pi is defined before the function is called, and it exists after the function finishes. In contrast, pi is local here:
=================================================== def circ(d) : pi = 3.14159 # pi is created each time circ is called answer = pi * diameter return answer . . . r = readFloat("Type radius of a circle: ") c = circumference(2 * r) # pi does not exist here; it cannot be referenced: area = r * r * pi # ???! ===================================================
A global variable that is read (but not updated) by a function body can be safely used in the function's pre- and postconditions; it acts just like an extra parameter to the function:
=================================================== pi = 3.14159 def circumference(diameter) : { pre diameter >= 0 ∧ pi > 3 post answer = pi * diameter return answer } answer = pi * diameter return answer ===================================================We must restrict all calls to the function so that the call does not use the global variable as the target of the function's answer. That is, c = circumference(5) is ok, and so is c = circumference(pi), but pi = circumference(5) is not.
In the Python language, every global variable that is updated by a function must be listed in the global line that immediately follows the function's header line. This is a good practice that we will follow.
Here is a simple but important example: a timer that counts down to zero. The init and tick methods maintain the global variable time.
=================================================== time = 0 { 1. time == 0 premise 2. time >= 0 algebra 1 # time shouldn't be negative } def tick() : { pre time >= 0 post time >= 0 return ans } global time # this line says the function can update time { 1. time >= 0 premise } if time != 0 : time = time - 1 { 1. time_old != 0 premise 2. time_old >= 0 premise 3. time_old > 0 algebra 1 2 4. time == time_old - 1 premise 5. time >= 0 algebra 3 4 } else : print "RING RING RING" { 1. time >= 0 premise } { 1. time >= 0 premise } ans = time return ans def init(starttime) : { pre starttime > 0 post time >= 0 } global time time = starttime { 1. starttime > 0 premise 2. time == starttime premise 3. time >= 0 algebra 1 2 } # Driver code starts here: run the timer { 1. time >= 0 premise } novar = init(10) { 1. time >= 0 premise } a = tick() { 1. time >= 0 premise } a = tick() { 1. time >= 0 premise } a = tick() { 1. time >= 0 premise } ===================================================Each of the functions can change variable time, and each time a function finished, there is a postcondition that time >= 0. We will learn that a property that repeats over and over is invariant. Here, it is invariant that time stays nonnegative.
This next example is similar and important: The procedure that withdraws money from a bank account must never let the balance go negative:
=================================================== balance = 100; # global variable in the module for the bank account def withdraw(howmuch) : """withdraw removes howmuch from balance""" { pre howmuch >= 0 ∧ balance >= 0 post balance == balance_in + cash ∧ balance >= 0 return cash } global balance if howmuch <= balance : cash = howmuch else : cash = balance balance = balance - cash return cash ===================================================withdraw's pre- and post-conditions are carefully written to ensure that the procedure is used correctly. Recall that balance_in means the value of global variable balance on procedure entry.
We will return to both these examples shortly.
Here is a precise statement of the law we use for procedures:
=================================================== g = ... # the global variable def f(x): { pre Q (where assertion Q mentions x and g) This is f_pre. post R (where R mentions ans, x, g, and g_in) This is f_post. return ans (this line is optional) } global g # notes that g can be updated by f BODY # does not assign to x but may assign to g return ans # this line is optional ===================================================To invoke the function, we prove the precondition:
=================================================== { [e/x]f_pre } y = f(e) # y and g do not appear in e, and y and g are distinct { 1. [y/ans][e/x][g_old/g_in]f_post premise 2. [y_old/y][g_old/g][e/x]f_pre premise ... } ===================================================Since global variable g acts as a second answer variable, g cannot appear in argument e nor can it be the same as y.
Earlier, we saw a timer module that maintained a nonnegative value for time. It is best to assert that time >= 0 is a global invariant. Then, each of the two functions can assume the invariant when they execute. (And, each function must ensure the invariant is still holding true when they exit!)
Here's the timer example, reworked:
=================================================== ### simple example of a global variable invariant time = 0 # we maintain this invariant property: { globalinv time >= 0 } # the function can use the invariant on entry, and the function # must ensure that the invariant holds on exit: def tick() : { pre True post ans == time return ans } global time # as needed, we introduce the globalinv in a proof: { 1. time >= 0 premise } if time != 0 : time = time - 1 { 1. time_old != 0 premise 2. time_old >= 0 premise 3. time_old > 0 algebra 1 2 4. time == time_old - 1 premise 5. time >= 0 algebra 3 4 } else : print "RING RING RING" { 1. time >= 0 premise } ans = time return ans def init(starttime) : { pre starttime > 0 post True } global time time = starttime ### Driver code starts here: run the timer # global invariant holds true novar = init(10) # invariant holds true: { 1. time >= 0 globalinv } a = tick() # invariant holds true: { 1. time >= 0 globalinv } a = tick() # invariant holds true: { 1. time >= 0 globalinv } a = tick() # and so on ... ===================================================
In an object-oriented language, when a global variable and its procedures ("methods") are defined within a class, the global-variable invariant is called the class invariant.
Here's an example, a class that is embedded in a toy bank into which a child inserts ten-cent coins. (The bank is a "reactive system that processes "coin-insertion events"!) The class enforces its invariant --- that's its mission.
=================================================== class DimesBank { # MAINTAINS A TOY BANK # fields in the class: dimes = 0 # how many coins inserted so far into the bank money = 0 # how much the coins are worth # the class maintains this invariant property: { globalinv money == dimes * 10 } # the handler method below can assume the invariant on entry, # and the method must ensure that the invariant holds on exit: def handleCoinInsertion(howmany) : """called when a child inserts howmany dimes into the bank""" { pre howmany >= 0 post True # the method enforces the invariant, that's it. } global dimes, money # we introduce the invariant as a premise when needed: { 1. money == dimes * 10 premise } dimes = dimes + howmany { 1. money == dimes_old * 10 premise 2. dimes == dimesold + howmany premise 3. money + (howmany * 10) == dimes * 10 algebra 1 2 } # the invariant is broken here, but the next command restores it: money = money + (howmany * 10) { 1. money_old + (howmany * 10) == dimes * 10 premise 2. money == moneyold + (howmany * 10) premise 3. money == dimes * 10 subst 2 1 } # } END CLASS # the following code might be in a controller component that # accepts input events and calls event handlers: coins = readInt("insert your coins and press the button!") assert coins > 0 novar = handleCoinInsertion(coins) # see the method above print money # on the toy bank's GUI ===================================================When you code a class, first decide on the class's fields (its "attributes") and write down the class invariant. Then, define and code the methods so that they maintain the invariant.
Here is a second example, a class that models a bank account. The account's balance should always be kept nonnegative:
=================================================== # A bank-account "class" and its maintenance methods. # The balance balance must be kept nonnegative. class BankAccount { # The global variable, the money in a bank balance: balance = 0 { 1. balance == 0 premise 2. balance >= 0 algebra 1 } # the global invariant: { globalinv balance >= 0 } # this property is currently true, and # we want the methods to preserve it def deposit(howmuch) : """deposit adds howmuch to balance""" { pre howmuch >= 0 post balance == balance_in + howmuch } # balance_in is the value of balance when the method was entered global balance { 1. balance >= 0 premise # the globalinv holds on entry 2. howmuch >= 0 premise # the precondition 3. balance == balance_in premise # the value of balance on entry 4. return 1 2 3 # remember all three facts for later } balance = balance + howmuch { 1. balance == balance_old + howmuch premise 2. balance_old >= 0 premise 3. howmuch >= 0 premise 4. balance_old == balance_in premise 5. balance == balance_in + howmuch subst 4 1 6. balance >= 0 algebra 1 2 3 7. return 5 6 } # The last line asserts that the global invariant is preserved # at the exit, and the postcondition is proved, too. def withdraw(howmuch) : """withdraw removes howmuch from balance""" { pre howmuch >= 0 post balance == balance_in + cash return cash } global balance { 1. balance >= 0 premise # the globalinv holds on entry 2. howmuch >= 0 premise # the precondition 3. balance == balance_in premise # the value of balance on entry 4. return 1 2 3 # remember all three facts for later } if howmuch <= balance : balance = balance - howmuch cash = howmuch { ... # prove here balance == balance_in + cash and balance >= 0 } else : cash = 0 { ... # prove here balance == balance_in + cash and balance >= 0 } { 1. balance == balance_in + cash and balance >= 0 premise } return cash def getBalance() : """getBalance returns the current balance""" { pre True post ans == balance return ans } ans = balance { 1. ans == balance premise } return ans } # END CLASS ===================================================All the class's methods pledge to keep balance nonnegative. Assuming that no other program commands update the balance, the proofs ensure that balance's global invariant holds always.
Whenever a module or class holds a data structure, there always should be a global invariant that states the critical properties of the structure that must be maintained. The procedures that maintain the data structure pledge to preserve its invariant.
This is the technique used in modular and object-oriented programming, where a data structure, its global invariant, and the data structure's maintenance procedures/methods are placed together. Since only the maintenance procedures update the global variable and preserve the invariant, the rest of the program can always rely on the global invariant to hold true. Without this convention, it is impossible to do component-based programming.
=================================================== g = ... # the global variable { globalinv I_g } # must be proved true here def f(x): { pre Q (where assertion Q mentions x and g) This is f_pre. post R (where R mentions ans, x, g, and g_in) This is f_post. return ans } global g # g can be updated by f { 1. Q premise 2. I_g premise ... } BODY # does not assign to x but may assign to g { R ∧ I_g } # we must prove both R and I_g on exit return ans ===================================================For the function's invocation, we deduce [e/x]pre_f to get the result. Since global variable g acts as a second answer variable, g cannot appear in argument e nor can it be the same as y.
=================================================== { [e/x]pre_f, that is, Q_e,g } y = f(e) # y and g do not appear in e, and y and g are distinct names { 1. [y/ans][e/x][g_old/g_in]f_post premise 2. [y_old/y][g_old/g][e/x]f_pre premise 3. I_g globalinv ... } ===================================================Further, provided that all assignments to global variable g occur only within functions that preserve its I_g, we can always assert I_g as needed in the main program.
There are many other examples of modules/classes that use global invariants. Here are three obvious ones:
For integer n > 0, the factorial of n, written n! is defined as 1*2*3*...up to...*n. It is the tradition to define 0! = 1, but factorials for negative integers do not exist.
Factorial lists the number of permutations (combinations or "shuffles"), e.g., fact(3) = 6 notes there six permutations for arranging three items, a, b, and c:
abc bac bca acb cab cbaThere is a profound recursive definition of factorial:
0! == 1 n! == (n-1)! * n, for n > 0For example, we calculate 4! like this:
=================================================== 4! == 3! * 4 where 3! == 2! * 3 where 2! == 1! * 2 where 1! == 0! * 1 where 0! == 1 So... 0! == 1 1! == 0! * 1 = 1 2! == 1! * 2 = 2 3! == 2! * 3 = 6 and finally, 4! == 3! * 4 = 24 ===================================================The process of counting downwards from 4! to 3! to 2! to 1! to 0! and assembling the answer as 1 * 1 * 2 * 3 * 4 can be programmed as a function that repeatedly calls itself for the answers to 3!, 2!, 1!, etc.:
=================================================== def fact(n) : # returns n! { pre n >= 0 post (to come) return ans } if n == 0 : ans = 1 # this computes 0! = 1 else : a = fact(n-1) # this computes (n-1)! ans = a * n # this computes n! = (n-1)! * n return ans ===================================================The easiest way to understand the computation of, say, fact(3), is to draw out the function calls, making a new copy of the called function each time it is restarted, like this:
=================================================== fact(3) => n = 3 if n == 0 : ans = 1 else : a = fact(n-1) ans = a * n return ans ===================================================Notice how the binding of argument 3 to parameter n is enacted with the assignment, n = 3. (This is how it is implemented within a computer, too.) The code for fact(3) activates a fresh copy of fact with argument 2:
=================================================== fact(3) => n = 3 a = fact(2) => n = 2 if n == 0 : ans = a * n ans = 1 return ans else : a = fact(n-1) ans = a * n return ans ===================================================This generates another call (fresh copy of) fact:
=================================================== fact(3) => n = 3 a = fact(2) => n = 2 ans = a * n a = fact(1) => n = 1 if n == 0 : return ans ans = a * 2 ans = 1 return ans else : a = fact(n-1) ans = a * n return ans ===================================================This expands to
=================================================== fact(3) => n = 3 a = fact(2) => n = 2 ans = a * n a = fact(1) => n = 1 return ans ans = a * n a = fact(0) => n = 0 if n == 0 : return ans ans = a * n ans = 1 return ans else : . . . return ans ===================================================We see a sequence, or ``stack,'' of activations of fact, one per call. Within a computer, an activation-record stack remembers the activations. The call to fact(0) returns an answer --- 1:
=================================================== fact(3) => n = 3 a = fact(2) => n = 2 ans = a * n a = fact(1) => n = 1 return ans ans = a * n a = fact(0) => return 1 return ans ans = a * n return ans ===================================================This makes the most recent activation (copy) of fact disappear, and the returned answer is assigned to ans in the previous call,
=================================================== fact(3) => n = 3 a = fact(2) => n = 2 ans = a * 3 a = fact(1) => n = 1 return ans ans = a * 2 a = 1 return ans ans = a * n return ans ===================================================allowing the previous call to return its answer to its caller:
=================================================== fact(3) => n = 3 a = fact(2) => n = 2 ans = a * 3 a = fact(1) => return 1 return ans ans = a * 2 return ans ===================================================You see the pattern --- the calls are finishing in reverse order, returning the partial answers, fact(0) = 1, fact(1) = 1, fact(2) = 2, and so on:
=================================================== fact(3) => n = 3 a = fact(2) => n = 2 ans = a * n a = 1 return ans ans = a * n return ans ===================================================and then
=================================================== fact(3) => n = 3 a = fact(2) => return 2 ans = a * n return ans ===================================================and
=================================================== fact(3) => n = 3 a = 2 ans = a * n return ans ===================================================and finally
=================================================== fact(3) => return 6 ===================================================Within the computer, the code for fact is not copied at each call --- instead, a new namespace (activation record) is generated for each call to fact. When a call finishes, its answer is returned and its namespace is erased (popped from the activation stack).
Every function has its own pre- and post- conditions. So, if a function calls itself, it can use its own pre- and postconditions to deduce the properties of the answer computed by the self-call. This is remarkable and exactly correct.
Here is the desired specification of fact:
def fact(n) { pre n >= 0 post ans == n! return ans }When fact calls itself, we use the above pre- and postconditions to deduce what happens. In the process, we deduce that the completed coding of fact possesses exactly these same pre- and postconditions!
Recall again the ``official definition'' of n!:
0! == 1 n! == (n-1)! * n, for n > 0Here is the deduction that fac meets its stated pre- and postconditions:
=================================================== def fact(n) : { pre n >= 0 post ans == n! return ans } if n == 0 : ans = 1 { 1. ans == 1 premise 2. n == 0 premise 3. ans == 0! definition of 0! == 1 4. ans == n! subst 2 3 } else : { 1. ¬(n == 0) premise 2. n > 0 premise 3. n - 1 >= 0 algebra 1 2 # this proves pre_fac } sub = fact(n-1) { 1. sub == (n-1)! premise } # post_fac ans = sub * n { 1. ans == sub * n premise 2. sub == (n-1)! premise 3. ans == (n-1)! * n subst 2 1 4. ans == n! definition of n! == (n-1)! * n } { 1. ans == n! premise } } return ans ===================================================We did it! The proof is not magic or trickery --- notice that the call, fact(n-1), uses an argument value that is different --- one smaller than --- argument n used with fact(n). This style of recursion by counting-downward-by-ones-until-zero will be exposed in the next chapter as an instance of mathematical induction.
Here is a sample call of the end result:
=================================================== { 200 >= 0, that is [200/n]pre_fact } x = fact(200) { [x/ans][200/n]post_fact, that is, x == 200! } ===================================================In the next chapter, we will see a strong connection between a function's self-call and a loop --- in both cases, the construct reuses its very own ``pre-post-condition'' when repeating itself.
=================================================== def f(x): { pre Q (where assertion Q can mention x) Call this, f_pre. post R (where assertion R can mention ans and x) Call this, f_post. return ans (the name of the answer variable) } { 1. Q premise } BODY # does not assign to x { ... prove R here, that is, ans and x have property R } ===================================================Outside of the function, f, we write f_pre for Q and f_post for R. Here is the schematic for a correct call of the function:
=================================================== { [e/x]f_pre } # prove the precondition, where e binds to parameter x y = f(e) # y does not appear in e { 1. [y/ans][e/x]f_post premise # presume the postcondition where # y receives the ans value 2. [y_old/y][e/x]f_pre premise . . . } ===================================================Recall that [e/x]f_pre defines a substitution of e for all occurrences of x within formula f_pre. The other two substitions explain how f_post is stated in terms of the receiving variable, y, and how the entry premise is propagated after the call.
=================================================== g = ... # the global variable { globalinv I_g } # must be proved true here def f(x): { pre Q (where assertion Q mentions x and g) This is f_pre. post R (where R mentions ans, x, g, and g_in) This is f_post. return ans } global g # g can be updated by f { 1. Q premise 2. I_g premise ... } BODY # does not assign to x but may assign to g { R ∧ I_g } # we must prove both R and I_g on exit return ans ===================================================For the function's invocation, we deduce [e/x]pre_f to get the result. Since global variable g acts as a second answer variable, g cannot appear in argument e nor can it be the same as y.
=================================================== { [e/x]pre_f, that is, Q_e,g } y = f(e) # y and g do not appear in e, and y and g are distinct names { 1. [y/ans][e/x][g_old/g_in]f_post premise 2. [y_old/y][g_old/g][e/x]f_pre premise 3. I_g globalinv ... } ===================================================Further, provided that all assignments to global variable g occur only within functions that preserve its I_g, we can always assert I_g as needed in the main program.
def f(x): { pre Q_x post R_ans,x return ans }if a call to f is meant to achieve a goal, G, we reason backwards from G to its subgoal like this:
{ subgoal: [e/x]pre_f ∧ ([e/x][y/ans]post_f —> G) } y = f(e) # y does not appear in argument e { goal: G }It is a good exercise to extend this backwards law to procedures. Try it.